Optical character recognition (OCR) systems are generally used to convert image information (e.g., scanned images, photos, etc.) containing text to machine-encoded data. In order to accurately recognize text with a conventional OCR engine, the image typically needs to be of a high quality. The quality of the image depends on various factors such as the power of the lens, light intensity variation, relative motion between the camera and text, focus, and so forth. Generally, an OCR engine can detect a majority of text characters in good quality images, such as images having uniform intensity, no relative motion, and good focus. However, even with good quality images, conventional OCR engines are still often unable to accurately detect all text characters.
With the introduction of more powerful and capable mobile computing devices (e.g., smartphones, phablets, tablet computing devices, etc.), applications that were traditionally found on desktop computing devices or servers are being implemented for running on mobile computing devices. For a given OCR implementation adapted for use on a mobile computing device, a set of challenges are presented as mobile computing devices are used in different physical environments and have a more limited set of resources that may require a more efficient OCR implementation to optimally run on a given mobile computing device. As technology advances and as people are increasingly using mobile computing devices in a wider variety of ways, it can be advantageous to adapt the ways in which images are processed by an OCR engine in order to improve text recognition.